FAIRE DES CARTES DE FLUX DANS R

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Les données

Jeu de données sur les migrations internationales. Migration Stock at subregional level, 2019 Source : United Nations, Department of Economic and Social Affairs, Population Division (2019). Voir

Les packages

install.packages("sf")
install.packages("remotes")
install.packages("smoothr")
library(remotes)
install_github("riatelab/mapsf")
install_github("tributetotobler/ttt")
library("sf")
library("mapsf")
library("ttt")

Import et mise en forme des données

Données géométriques

countries <- st_read("data/world/geom/countries.gpkg")
graticule <- st_read("data/world/geom/graticule.gpkg")
bbox <- st_read("data/world/geom/bbox.gpkg")

crs <-
  "+proj=aeqd +lat_0=90 +lon_0=50 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs "
countries <- st_transform(x = countries, crs = crs)
graticule <- st_transform(x = graticule, crs = crs)
bbox <- st_transform(x = bbox, crs = crs)
land <- st_union(countries)

Données attributaires

migr <- read.csv("data/world/fij/migr2019_T.csv")

Template cartographique

col = "#ffc524"
credit = paste0(
  "Françoise Bahoken & Nicolas Lambert, 2021\n",
  "Source: United Nations, Department of Economic\n",
  "and Social Affairs, Population Division (2019)"
)
# theme = mf_theme(x = "default", bg = "white", tab = FALSE,
#                    pos = "center", line = 2, inner = FALSE,
#                    fg = "#9F204270", mar = c(0,0, 2, 0),cex = 1.9)

theme <- mf_theme(
  x = "default",
  bg = "#3b3b3b",
  fg = "#ffc524",
  mar = c(0, 0, 2, 0),
  tab = TRUE,
  pos = "left",
  inner = FALSE,
  line = 2,
  cex = 1.9,
  font = 3
)

template = function(title, file) {
  mf_export(
    countries,
    export = "png",
    width = 1000,
    filename = file,
    res = 96,
    theme = theme,
    expandBB = c(-.02, 0, -.02, 0)
  )
  mf_map(
    bbox,
    col = "#3b3b3b",
    border = NA,
    lwd = 0.5,
    add = TRUE
  )
  mf_map(graticule,
         col = "#FFFFFF50",
         lwd = 0.5,
         add = TRUE)
  mf_map(
    countries,
    col = "#4e4f4f",
    border = "#3b3b3b",
    lwd = 0.5,
    add = TRUE
  )
  # mf_map(links, col = NA,border = "#317691", lwd = 0.5, add = TRUE)
  mf_credits(
    txt = credit,
    pos = "bottomright",
    col = "#1a2640",
    cex = 0.7,
    font = 3,
    bg = "#ffffff30"
  )
  mf_title(title)
}
template("Template cartographique", "maps/template.png")
dev.off()

Ce qu’on peut faire en R base & mapsf

L’effet Spaghetti

links <-
  mf_get_links(
    x = countries,
    df = migr,
    x_id = "adm0_a3_is",
    df_id = c("i", "j")
  )
template("L'effet Spaghetti ", "maps/spaghetti.png")
mf_map(links, col = col, add = TRUE)
mf_map(land,
       col = NA,
       border = "#3b3b3b",
       add = TRUE)
dev.off()

Sélectionner un seul pays

Choix d’un pays

ISO3 <- "FRA"
label = "France"

Jointure et mise en forme des données

countr <- countries[, c("adm0_a3_is", "label")]
migrFRA <- migr[migr$j == ISO3, ]
migrFRA$fij <- as.numeric(migrFRA$fij)
maxval = max(migrFRA$fij)
total = round(sum(migrFRA$fij) / 1000000,1)
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "i",
    all.x = TRUE
  )
countr <- countr[-3]
colnames(countr) <- c("id", "label", "fij", "geometry")
knitr::kable(countr[c(0:10),], row.names = F, digits = 1)
id label fij geometry
ABW Aruba 11 MULTIPOLYGON (((-7476945 42…
AFG Afghanistan 6887 MULTIPOLYGON (((2474775 -53…
AGO Angola 23438 MULTIPOLYGON (((-4917506 -1…
AIA Anguilla 10 MULTIPOLYGON (((-7351488 31…
ALB Albania 7371 MULTIPOLYGON (((-2639654 -4…
AND Andorra 1079 MULTIPOLYGON (((-3952645 -3…
ARE United Arab Emirates 862 MULTIPOLYGON (((785851 -712…
ARG Argentina 14253 MULTIPOLYGON (((-14113355 7…
ARM Armenia 21012 MULTIPOLYGON (((-348529.3 -…
ASM American Samoa 1 MULTIPOLYGON (((7561304 878…

Une première carte simple

template(paste0("En 2019, il y avait ",total, " millions d'étrangers en France"),
         "maps/prop1.png")
#mf_map(countr[countr$id == ISO3,], col = col, border = "red", lwd = 2, add = TRUE)
mf_map(
  countr[countr$id != ISO3, ],
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  val_max = maxval,
  inches = 0.4,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countr[countr$id == ISO3, ],
  col = NA,
  border = "#e36019",
  lwd = 2,
  add = TRUE
)
dev.off()

La carte symétrique

countr <- countries[, c("adm0_a3_is", "label")]
migrFRA <- migr[migr$i == ISO3, ]
migrFRA$fij <- as.numeric(migrFRA$fij)
total = round(sum(migrFRA$fij) / 1000000,1)
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "j",
    all.x = TRUE
  )
countr <- countr[-3]
colnames(countr) <- c("id", "label", "fij", "geometry")
template(paste0("En 2019, il y avait ",total, " millions de Français à l'étranger"),
         "maps/prop2.png")
mf_map(
  countr[countr$id != ISO3, ],
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  val_max = maxval,
  inches = 0.4,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countr[countr$id == ISO3, ],
  col = NA,
  border = "#e36019",
  lwd = 2,
  add = TRUE
)
dev.off()

On peut faire la même carte en faisant varier l’épaisseur des liens

ISO3 <- "FRA"
label = "France"
migrtoFRA <- migr[migr$j == ISO3,]
migrtoFRA$fij <- as.numeric(migrtoFRA$fij)
links <-
  mf_get_links(
    x = countries,
    df = migrtoFRA,
    x_id = "adm0_a3_is",
    df_id = c("i", "j")
  )
template(
  paste0("Origine des personnes migrantes vivant en ", label, " en 2019"),
  "maps/links1.png"
)
mf_map(
  links,
  var = "fij",
  col = col,
  border = "white",
  type = "prop",
  inches = 10,
  leg_title_cex = 1.2,
  leg_val_cex   = 0.8,
  leg_pos = "bottomleft",
  leg_title = "Nombre de personnes"
)
mf_map(
  countries[countries$adm0_a3_is == ISO3,],
  col = "#4e4f4f",
  border = col,
  lwd = 1.5,
  add = TRUE
)
dev.off()

Une carte un peu plus sophistiquée avec packcircles

ISO3 <- "FRA"
label = "France"
migrFRA <- migr[migr$j == ISO3,]
migrFRA$fij <- as.numeric(migrFRA$fij)
migrFRA <-
  rbind.data.frame(migrFRA, c(
    i = ISO3,
    j = ISO3,
    fij = sum(as.numeric(migrFRA$fij))
  ))
countr <- countries[, "adm0_a3_is"]
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "i",
    all.x = TRUE
  )
colnames(countr) <- c("i", "j", "fij", "geometry")
knitr::kable(countr[c(0:10),], row.names = F, digits = 1)
i j fij geometry
ABW FRA 11 MULTIPOLYGON (((-7476945 42…
AFG FRA 6887 MULTIPOLYGON (((2474775 -53…
AGO FRA 23438 MULTIPOLYGON (((-4917506 -1…
AIA FRA 10 MULTIPOLYGON (((-7351488 31…
ALB FRA 7371 MULTIPOLYGON (((-2639654 -4…
AND FRA 1079 MULTIPOLYGON (((-3952645 -3…
ARE FRA 862 MULTIPOLYGON (((785851 -712…
ARG FRA 14253 MULTIPOLYGON (((-14113355 7…
ARM FRA 21012 MULTIPOLYGON (((-348529.3 -…
ASM FRA 1 MULTIPOLYGON (((7561304 878…

Cercles avec packcircles (Dorling style)

library(packcircles)
dots = countr
st_geometry(dots) <-
  st_centroid(sf::st_geometry(dots), of_largest_polygon = TRUE)
dots <- data.frame(dots$i, dots["fij"], st_coordinates(dots))
dots = dots[, c("dots.i", "X", "Y", "fij")]
colnames(dots) <- c("id", "x", "y", "v")
dots <- dots[!is.na(dots$v), ]

k = 700000 # pour ajuster la taille des cercles
itermax = 10 # nombre d'iterations
delta = 35000
dat.init <- dots[, c("x", "y", "v", "id")]
dat.init$v <- sqrt(as.numeric(dat.init$v) * k)
simulation <- circleRepelLayout(
  x = dat.init,
  xysizecols = 1:3,
  wrap = FALSE,
  sizetype = "radius",
  maxiter = itermax,
  weights = 1
)$layout
circles <- st_buffer(sf::st_as_sf(
  simulation,
  coords = c('x', 'y'),
  crs = sf::st_crs(countries)
),
dist = simulation$radius - delta)

circles$v = dots$v
circles$id = dots$id

Links

# Links

dots$j = "FRA"

links <-
  mf_get_links(
    x = circles,
    df = migrFRA,
    x_id = "id",
    df_id = c("i", "j")
  )
links$fij = as.numeric(links$fij)

Réalisation de la carte

template("Les étrangers en France, 2019", "maps/migrexplorer1.png")

col2 = "#4e4f4f"

mf_map(
  land,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)


mf_map(
  links,
  var = "fij",
  col = col,
  border = "#3b3b3b",
  type = "prop",
  lwd_max = 160,
  leg_pos = "n",
  add = TRUE
)

mf_map(
  circles[circles$id != ISO3, ],
  var = "fij",
  col = col,
  border = "#3b3b3b",
  lwd = 1.5,
  add = TRUE
)

mf_map(
  circles[circles$id == ISO3, ],
  var = "fij",
  col = col2,
  border = col,
  lwd = 2.5,
  add = TRUE
)

t =  circles[circles$id != ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col2,
  overlap = TRUE,
  lines = FALSE
)

t =  circles[circles$id == ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col,
  overlap = TRUE,
  lines = FALSE
)

dev.off()

Comme précédemment, on peut faire la carte en symétrie en inversant i et j.

ISO3 <- "FRA"
label = "France"
migrFRA <- migr[migr$i == ISO3,] # ici
migrFRA$fij <- as.numeric(migrFRA$fij)
migrFRA <-
  rbind.data.frame(migrFRA, c(
    i = ISO3,
    j = ISO3,
    fij = sum(as.numeric(migrFRA$fij))
  ))
countr <- countries[, "adm0_a3_is"]
countr <-
  merge(
    x = countr,
    y = migrFRA,
    by.x = "adm0_a3_is",
    by.y = "j", # là
    all.x = TRUE
  )
colnames(countr) <- c("i", "j", "fij", "geometry")
dots = countr
st_geometry(dots) <-
  st_centroid(sf::st_geometry(dots), of_largest_polygon = TRUE)
dots <- data.frame(dots$i, dots["fij"], st_coordinates(dots))
dots = dots[, c("dots.i", "X", "Y", "fij")]
colnames(dots) <- c("id", "x", "y", "v")
dots <- dots[!is.na(dots$v), ]

k = 700000 # pour ajuster la taille des cercles
itermax = 10 # nombre d'iterations
delta = 35000
dat.init <- dots[, c("x", "y", "v", "id")]
dat.init$v <- sqrt(as.numeric(dat.init$v) * k)
simulation <- circleRepelLayout(
  x = dat.init,
  xysizecols = 1:3,
  wrap = FALSE,
  sizetype = "radius",
  maxiter = itermax,
  weights = 1
)$layout
circles <- st_buffer(sf::st_as_sf(
  simulation,
  coords = c('x', 'y'),
  crs = sf::st_crs(countries)
),
dist = simulation$radius - delta)

circles$v = dots$v
circles$id = dots$id

Links

# Links

dots$j = "FRA"

links <-
  mf_get_links(
    x = circles,
    df = migrFRA,
    x_id = "id",
    df_id = c("i", "j")
  )
links$fij = as.numeric(links$fij)

Réalisation de la carte

template("Les français à l'étranger, 2019", "maps/migrexplorer2.png")

col2 = "#4e4f4f"

mf_map(
  land,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)


mf_map(
  links,
  var = "fij",
  col = col,
  border = "#3b3b3b",
  type = "prop",
  lwd_max = 160,
  leg_pos = "n",
  add = TRUE
)

mf_map(
  circles[circles$id != ISO3, ],
  var = "fij",
  col = col,
  border = "#3b3b3b",
  lwd = 1.5,
  add = TRUE
)

mf_map(
  circles[circles$id == ISO3, ],
  var = "fij",
  col = col2,
  border = col,
  lwd = 2.5,
  add = TRUE
)

t =  circles[circles$id != ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col2,
  overlap = TRUE,
  lines = FALSE
)

t =  circles[circles$id == ISO3, ]
mf_label(
  t,
  var = "id",
  halo = FALSE,
  cex = sqrt(as.numeric(t$v) / 1200000),
  col = col,
  overlap = TRUE,
  lines = FALSE
)

dev.off()

Ces cartes, on peut les retrouver dans l’application MigrExplorer mise en ligne via R shiny.

https://analytics.huma-num.fr/Nicolas.Lambert/migrexplorer/

https://gitlab.huma-num.fr/nlambert/migrexplorer/-/tree/master

Changer de maillage

Contrairement aux cartes pays * pays, cartographier les flux au niveau régional permet de mieux percevoir la logique des mobilités internationales. Cette carte, pas très élégantes, a été réalisée et présentée par François Héron pour ses cours au Collège de France.

Et si on esseyait de la reproduire en R ?

migr <- read.csv("data/world/subregions/migrantstocks2019.csv")
subregions <- st_read("data/world/geom/subregions.gpkg") %>% st_transform(crs)
template("Subregions", "maps/subregions.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = col,
  lwd = 0.5,
  add = TRUE
)
mf_label(
  x = subregions,
  var = "label",
  halo = TRUE,
  bg = "#4e4f4f",
  cex = 0.8,
  col = col,
  overlap = TRUE,
  lines = FALSE
)
dev.off()

Flowmapper

flowmapper() est une fonction du package ttt (en cours de développement).

library(ttt)

La fonction ttt_flowmapper() prends plusieurs arguements :

…

Les données

migr <- read.csv("data/world/subregions/migrantstocks2019.csv")
threshold <- 1500
migr <- migr[migr$fij >= threshold, ]
knitr::kable(migr[c(0:10),], row.names = F, digits = 1)
i j fij
5500 923 5603
5501 5501 11177
5501 918 5334
5501 920 1666
5501 922 18402
5501 924 2551
906 906 5202
906 918 5700
910 910 5330
910 913 1538 
flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  plot = FALSE
)

Liens

template("ttt_flowmapper$links", "maps/ttt_links.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$links,
       col = col,
       lwd = 3,
       add = TRUE)
dev.off()

Cercles

template("ttt_flowmapper$circles", "maps/ttt_circles.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$circles, col = col, add = TRUE)
dev.off()

Flêches

template("ttt_flowmapper$flows", "maps/ttt_flows.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
mf_map(flows$flows, col = col, add = TRUE)
dev.off()

Visualisation par défaut

template("flowmappze", "maps/ttt_flows.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
ttt_flowmapperlegend(x = flows, title = "Flux", col = col)
dev.off()

La VV taille, c’est aussi la surface

template("La surface des fleches", "maps/ttt_surface.png")
mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)
ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  size = "area",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
dev.off()

Epaisseur vs Surface

Interactions (type = “rect”)

migr2 <- data.frame(i = integer(), j = integer(), fij = integer())

for (k in 1:length(migr$i)) {
  val1 <- migr$fij[k]
  val2 <- migr[migr$i == migr$j[k] & migr$j == migr$i[k], "fij"]
  val <- sum(val1, val2)
  idi =  migr$i[k]
  idj =  migr$j[k]
  test <-
    length(migr2[(migr2$i == idi &
                    migr2$j == idj) | (migr2$i == idj & migr2$j == idi), "fij"])
  if (test == 0) {
    migr2 <- rbind(migr2, data.frame(i = idi, j = idj, fij = val))
  }
}
migr2 <- migr2[migr2$i != migr2$j, ] 
head(migr2)
##      i   j   fij
## 1 5500 923  9999
## 3 5501 918  5334
## 4 5501 920  3221
## 5 5501 922 18402
## 6 5501 924  2551
## 8  906 918  5700
template("tInteractions", "maps/ttt_interactions.png")
c <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  size = "thickness",
  type = "rect",
  df = migr2,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = col,
  border = "#424242",
  border2 = col,
  add = TRUE
)
dev.off()

Combiner flux intra et flux inter

intra <- migr[migr$i == migr$j, ]
intra <- intra[, c("i", "fij")]
colnames(intra) <- c("id", "nb")
knitr::kable(intra, row.names = F, digits = 1)
template("Flux inter et flux intra", "maps/interintra.png")
flows <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  size = "thickness",
  type = "arrows",
  decreasing = FALSE,
  add = TRUE,
  lwd = 1,
  col = col,
  border = "#424242",
  k = NULL,
  k2 = 60,
  df2 = intra,
  df2id = "id",
  df2var = "nb",
  col2 = "#eb4034",
  border2 = "#424242"
)
dev.off()

Reprojection

1 - calcul en projection polaire

tmp <- ttt_flowmapper(
  x = subregions,
  xid = "id",
  type = "arrows",
  df = migr,
  dfid = c("i", "j"),
  dfvar = "fij",
  col = "#ffc524",
  border = "#424242",
  border2 = "#ffc524",
  plot = FALSE
)

2 - reprojection & nouveau template

crs <-
  "+proj=ortho +lat_0=42.5333333333 +lon_0=-72.53333333339999 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"
flows <- smoothr::densify(tmp$flows, n = 30) %>% st_transform(crs)
dots <- st_transform(tmp$circles, crs)
subregions <- st_transform(subregions, crs)
graticule <- st_transform(graticule, crs)
bbox <- st_transform(bbox, crs)

3 - affichage

title = "Flux sur Globe"
file =   "maps/ttt_globe.png"

mf_export(
  subregions,
  export = "png",
  width = 1000,
  filename = file,
  res = 96,
  theme = theme,
  expandBB = c(-.02, 0,-.02, 0)
)

mf_map(
  bbox,
  col = "#3b3b3b",
  border = NA,
  lwd = 0.5,
  add = TRUE
)

mf_map(graticule,
       col = "#FFFFFF50",
       lwd = 0.5,
       add = TRUE)

mf_map(
  subregions,
  col = "#4e4f4f",
  border = "#3b3b3b",
  lwd = 0.5,
  add = TRUE
)

mf_credits(
  txt = credit,
  pos = "bottomright",
  col = "#1a2640",
  cex = 0.7,
  font = 3,
  bg = "#ffffff30"
)

mf_map(flows, col = col, add = TRUE)

mf_map(dots, col = col, add = TRUE)

mf_title(title)

dev.off()

Vers une implémantation dans Observable (svg/d3js)

Ouvrir l’application

A vous de jouer